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57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
import argparse
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import azureml.train.automl
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from azureml.automl.core._vendor.automl.client.core.runtime import forecasting_models
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from azureml.core import Run
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from sklearn.externals import joblib
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import forecasting_helper
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--max_horizon', type=int, dest='max_horizon',
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default=10, help='Max Horizon for forecasting')
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parser.add_argument(
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'--target_column_name', type=str, dest='target_column_name',
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help='Target Column Name')
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parser.add_argument(
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'--time_column_name', type=str, dest='time_column_name',
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help='Time Column Name')
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parser.add_argument(
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'--frequency', type=str, dest='freq',
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help='Frequency of prediction')
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args = parser.parse_args()
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max_horizon = args.max_horizon
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target_column_name = args.target_column_name
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time_column_name = args.time_column_name
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freq = args.freq
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run = Run.get_context()
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# get input dataset by name
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test_dataset = run.input_datasets['test_data']
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grain_column_names = []
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df = test_dataset.to_pandas_dataframe()
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X_test_df = test_dataset.drop_columns(columns=[target_column_name])
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y_test_df = test_dataset.with_timestamp_columns(
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None).keep_columns(columns=[target_column_name])
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fitted_model = joblib.load('model.pkl')
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df_all = forecasting_helper.do_rolling_forecast(
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fitted_model,
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X_test_df.to_pandas_dataframe(),
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y_test_df.to_pandas_dataframe().values.T[0],
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target_column_name,
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time_column_name,
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max_horizon,
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freq)
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file_name = 'outputs/predictions.csv'
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export_csv = df_all.to_csv(file_name, header=True)
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# Upload the predictions into artifacts
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run.upload_file(name=file_name, path_or_stream=file_name)
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